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Frequency-informed transformer for real-time water pipeline leak detection

Autonomous Intelligent Systems 2025年第1期5卷 1-10页

作者： Liu, Fengnian Wang, Ding Tang, Junya Wang, Lei College of Electronic and Information Engineering Tongji University Shanghai201804 China School of Computer Science and Technology Tongji University Shanghai201804 China Shanghai KARON Eco-valve Manufacturing Co. ltd Shanghai201804 China

Water pipeline leaks pose significant risks to urban infrastructure, leading to water wastage and potential structural damage. Existing leak detection methods often face challenges, such as heavily relying on the manual selection of frequency bands or complex feature extraction, which can be both labour-intensive and less effective. To address these limitations, this paper introduces a Frequency-Informed Transformer model, which integrates the Fast Fourier Transform and self-attention mechanisms to enhance water pipe leak detection accuracy. Experimental results show that FiT achieves 99.9% accuracy in leak detection and 98.7% in leak type classification, surpassing other models in both accuracy and processing speed, with an efficient response time of 0.25 seconds. By significantly simplifying key features and frequency band selection and improving accuracy and response time, the proposed method offers a potential solution for real-time water leak detection, enabling timely interventions and more effective pipeline safety management. © The Author(s) 2025.

关键词： Discriminant analysis

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Efficient Cloud Resource Scheduling with an Optimized Throttled Load Balancing Approach

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computers, Materials & Continua 2023年第11期77卷 2179-2188页

作者： V.Dhilip Kumar J.Praveenchandar Muhammad Arif Adrian Brezulianu Oana Geman Atif Ikram Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai600062India Department of Computer Science and Engineering Karunya Institute of Technology and SciencesCoimbatoreTamil Nadu641114India Department of Computer Science Superior UniversityLahorePakistan Faculty of Electronics Telecommunications and Information Technology Gheorghe Asachi Technical University of IasiIa¸si700050Romania Department of Health and Human Development Stefan cel Mare University of SuceavaSuceava720229Romania Faculty of Ocean Engineering Technology and Informatics University Malaysia TerengganuKuala NerusMalaysia

Cloud technology is a new platform that offers on-demand computing Peripheral such as storage,processing power,and other computer system *** is also referred to as a system that will let the consumers utilize computational resources like databases,servers,storage,and intelligence over the *** a cloud network,load balancing is the process of dividing network traffic among a cluster of available servers to increase *** is also known as a server pool or server *** a single node is overwhelmed,balancing the workload is needed to manage unpredictable *** load balancer sends the load to another free node in this *** focus on the Balancing of workflows with the proposed approach,and we present a novel method to balance the load that manages the dynamic scheduling *** of the preexisting load balancing techniques is considered,however it is somewhat modified to fit the scenario at *** on the experimentation’s findings,it is concluded that this suggested approach improves load balancing consistency,response time,and throughput by 6%.

关键词： Load balancing throttled algorithm efficient resource allocation

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Research on the Transient Potential Rise Characteristics of High - speed Railway Rail Lightning Strike and Its Impact on Trackside Signal Equipment 37

Research on the Transient Potential Rise Characteristics of ...

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37th International Conference on Lightning Protection, ICLP 2024

作者： Wang, Shulai Xiang, Nianwen Wang, Chaoqun Lyu, Zengwei Chen, Zhiwei School of Electrical Engineering and Automation Hefei University of Technology Hefei China Institute of Energy Hefei Comprehensive National Science Center Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9783800764174

Lightning strikes on the catenary will cause a sharp rise in rail potential, which directly threatens the safety of the signal equipment connected to the rail along the trackside. It is urgent to carry out research on the propagation law of transient overvoltage on rails caused by lightning strikes on the catenary. A rail lightning electromagnetic transient calculation model considering the influence of integrated grounding system and catenary is established, and the influence law of different lightning strike point positions, different pier heights, and different lightning current waveforms on the distribution of rail potential rise is calculated. The specific conclusions are as follows: when lightning strikes different positions within a catenary span, the rail potential within 700m near the lightning strike point will exceed the lightning strike tolerance level of the trackside signal equipment, causing equipment damage. The potential rise increases by about 9 kV;for every 5 m increase in pier height, the rail potential rise increases by about 16%. Based on the law of the rail potential rise, the common-mode protection methods for the trackside signal equipment are proposed by appropriately reducing the horizontal connection distance of uplink and downlink ground wires to decrease the rail potential and installing common-mode protection devices. © 2024 IEEE, © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Signaling equipment

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Hybrid Adam Coati Optimization with DLSTM for Biomedical Parameter Prediction Using Sensor 3

Hybrid Adam Coati Optimization with DLSTM for Biomedical Par...

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3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Rajesh, L. Mohan, H.S. Bindiya, M.K. Visvesvaraya Technological University Dayananda Sagar Academy Of Technology And Management Department Of Information Science And Engineering Karnataka Bengaluru India Karnataka Bengaluru India Sjb Institute Of Technology Department Of Computer Science And Engineering Karnataka Bengaluru India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318609

A real-time patient monitoring system procesfses a wide range of biomedical parameters and can store and safely capture biomedical parameters. It lowers the diagnosis time of the several diseases that are prevalent in healthcare services. Accurate prediction of biomedical parameters can significantly reduce healthcare costs and assist in effective decision-making. This research proposes a deep learning approach with hybrid optimization for biomedical parameter prediction by using sensors. At first, a healthcare centre with many patients having various disorders is considered. Several biomedical sensors are placed on the patients and these sensors include a respiratory Rate (RR) sensor, Heart rate (HR) sensor, Pulse Rate (PR) sensor, Ambulatory Blood Pressure (ABP)-sensor, and Saturation of Peripheral Oxygen (SpO2). Based on these sensed parameters, biomedical parameter prediction by using sensor based Deep Long Short-Term Memory (DLSTM)is executed. The DLSTM is trained using the proposed Hybrid Adam Coati Optimization(HAdCO). Here, the HAdCO is developed by incorporating of Coati Optimization Algorithm (COA) with Adam Optimizer. Furthermore, the HAdCO-DLSTM is evaluated by utilising metrics, like Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Squared Error (MSE). The HAdCO-DLSTM had a minimal value of MAE at 0.0063, MSE at 0.0062, and RMSE at 0.0078. © 2024 IEEE.

关键词： Long short-term memory

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Hybrid CNN-LSTM Transfer Learning for Dengue Diagnosis from Raman Spectroscopy Images 5

Hybrid CNN-LSTM Transfer Learning for Dengue Diagnosis from ...

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5th IEEE International Conference on Telecommunications and Photonics, ICTP 2023

作者： Ahmmed, Syed Das, Sanchita Rani Leon, Mazharul Islam Rubaiyat Hossain Mondal, M. Institute of Information and Communication Technology Bangladesh University of Engineering and Technology Dhaka Bangladesh United International University Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350393477

The diagnosis of dengue fever can be made quickly and effectively with Raman spectroscopy. However, the diagnostic procedure presents difficulties;for example, the distinction between healthy and infected spectra is extremely subtle. Deep learning (DL)-based diagnosis of dengue from Raman spectroscopy is gaining research interest. This paper introduces an innovative hybrid model that combines the architecture of a long-short-term memory recurrent neural network (LSTM RNN) with the capabilities of a pre-trained convolutional neural network (CNN). We present the CNN-LSTM Transfer Learning (HCL-TL) method, which uses Raman spectroscopy images to transform the field of data-driven dengue disease diagnosis. The proposed approach utilizes hyperparameters to refine the DL models. Both the initial training and subsequent fine-tuning stages incorporate model checkpoints and callbacks to monitor loss and prevent overfitting. A dataset consisting of 635 spectra-460 of which were infected and 175 of which were healthy-was used to evaluate the suggested model. Both holdout and 10-fold cross-validation methods are used for splitting the data samples. When applied to testing data for dengue infection diagnosis, the proposed HCL- TL method achieves a classification accuracy of 97.73%. The HCL-TL method outperforms the state-of-the-art DL and machine learning models in terms of accurately classifying dengue fever from Raman spectroscopy images, according to the results. © 2023 IEEE.

关键词： Raman spectroscopy

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Classification in Rice Genome using Artificial Intelligence 6

Classification in Rice Genome using Artificial Intelligence

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6th International Conference on Computational Intelligence and Networks, CINE 2024

作者： Swain, Debabrata Kumar, Manish Swain, Nirmal Keshari Kulkarni, Chinmay Bhilare, Amol Pandit Deendayal Energy University Computer Science and Engineering Department Gujarat India Pandit Deendayal Energy University Electronics and Communication Engineering Department Gujarat India Vardhaman College of Engineering Department of Information Technology Telengana India University of Cumberlands Department of Information Technology WilliamsburgKY United States Vishwakarma Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798331516796

Epigenetic modifications, like N6methyladenine (6 mA), regulate genetic configuration and impact the cellular tasks. Recognizing the dissemination of 6mA sites within the rice genome is required for deciphering its influence on plant progression. In the proposed work, a detailed investigation of 6 mA sites in the rice genome using advanced artificial intelligence (AI) techniques. In this research AI models are trained on vast genetic data to correctly categorize 6 mA sites. The classification result has shown the detailed useful implication of 6 mA adjustments, shedding light on possible controlling elements and their roles in important biological procedures. © 2024 IEEE.

关键词： Genome

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Prediction of Steel Plate Fault Classification Using CART Fuzzy Logic and ANFIS Models 9

Prediction of Steel Plate Fault Classification Using CART Fu...

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9th International Conference on information technology Trends, ITT 2023

作者： Akpinar, Mustafa Adak, M. Fatih Higher Colleges of Technology Computer and Information Science Sharjah United Arab Emirates Sakarya University Computer Engineering Department Sakarya Turkey

ISBN: (纸本)9798350327502

Some systems correct faulty production to reduce the cost and increase the performance of steel plates. The first element of quality production is to identify and classify defects. However, when the error classification is not done well, the cost of correcting the error increases. This study proposes a classification and regression tree (CART) based fuzzy model to determine which fault class the faulty steel plates fall into. The second model used in the study is the adaptive neuro-fuzzy interface system (ANFIS). In both approaches, error classes were determined using fuzzy logic. In the two models created, it was observed that when the information of the detected faulty steel plate was entered, it was unsuccessful in determining which class this fault belonged to. Although it suggests a quick solution for detecting the error class, it has been seen that these approaches are not appropriate to use because they do not offer the right solution. © 2023 IEEE.

关键词： Errors

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Analysis of Various Motion Detection and Facial Recognition Based Home Security Systems 2nd

Analysis of Various Motion Detection and Facial Recognition ...

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2nd International Conference on Deep sciences for Computing and Communications, IconDeepCom 2023

作者： Karthikeyan, P. Nidhi, Shashank Vishnu Kumar, S.R. Karunakaran, V. Velliangiri, S. Department of Computer Science and Information Engineering National Chung Cheng University Chiayi Taiwan Department of Computer Science and Engineering-AI and ML Jain Deemed to be University Bangalore India Department of Computational Intelligence SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (纸本)9783031689079

A combination of AI and IoT makes a great home security system. IoT collects data from the surroundings, while AI takes the data and makes predictions accordingly. In this case, these predictions help in creating alerts. CCTV cameras were traditionally used for home security systems, with attendees required to monitor the footage. Because of technological advancements, this process can be completed with the least amount of human intervention. Other work has been done in the field of home security systems that use facial recognition and motion detection. Those systems don’t exhibit good accuracy in detecting multiple human faces. This work focuses on a detailed survey of different facial recognition techniques along with motion detection techniques as per the requirements of a home security system. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Motion tracking

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An Effective Feature Selection Technique for Detecting Parkinson's Disease Using Binary Whale Optimization Algorithm

An Effective Feature Selection Technique for Detecting Parki...

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2023 IEEE World Conference on Applied Intelligence and Computing, AIC 2023

作者： Chaudhuri, Abhilasha Mohdiwale, Samrudhi Op Jindal University Department of Computer Science and Engineering Raigarh India Siksha O Anusandhan University Department of Computer Science and Information Technology Bhubaneshwar India

ISBN: (纸本)9798350310061

Parkinson's disease is a neurological condition that affects the nerves of the body and results in uncontrollable body movements like shaking, stiffness etc. Parkinson's disease affects around 10 million people worldwide. Early detection of the disease is very important for patients. The easiest way to detect the disease is with the help of the voice recordings i.e., speech signals. As the speech dataset has very large number of features it is important to choose the optimal feature subset for disease classification. This task is known as feature selection. This work proposes a feature selection approach for efficient detection of the Parkinson's disease based on binary whale optimization algorithm. Eight different transfer functions have been tried and the best one is chosen. The result has been evaluated based on classification accuracy, feature selection ratio and the execution time metrics. A significant improvement of 7.7 % have been achieved over the classical method. © 2023 IEEE.

关键词： Feature Selection

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Graph-based anomaly detection using regression on HTTP 2

Graph-based anomaly detection using regression on HTTP

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2nd International Symposium on computer technology and information science, ISCTIS 2022

作者： Wu, Han Jiang, Zhupeng He, Fengyu Computer Science and Engineering Beijing Information Science Technology University Beijing China Software Engineering Beijing Information Science Technology University Beijing China

ISBN: (纸本)9781510660533

Detecting anomalies in HTTP request data is a vital security task. With big data becoming ubiquitous, techniques for structured graph data have been focused on recent years. As nodes in graphs have long-distance correlations, detecting anomaly in plain structured graph data is practical. This paper proposes a node-level feature-based regression detection method. Given a graph generated from a snapshot of HTTP request data collected by API gateway and considering clustering coefficient and empirical inspired rules, construct a regression model to dig out substantially deviate nodes. Extensive experimental studies on a real-world request dataset demonstrate that it performs relatively prominent and favorably to HBOS (a concurrent density-based method) and iForest (a linear time complexity model-based method with a low memory requirement) in terms of ROC-AUC and processing time. © 2022 SPIE.

关键词： Big data

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